US20170270644A1 - Depth image Denoising Method and Denoising Apparatus - Google Patents

Depth image Denoising Method and Denoising Apparatus Download PDF

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US20170270644A1
US20170270644A1 US15/502,791 US201615502791A US2017270644A1 US 20170270644 A1 US20170270644 A1 US 20170270644A1 US 201615502791 A US201615502791 A US 201615502791A US 2017270644 A1 US2017270644 A1 US 2017270644A1
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depth image
depth
denoising
original
region
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Jibo Zhao
Xingxing Zhao
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BOE Technology Group Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G06T5/002—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/60—Editing figures and text; Combining figures or text
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds

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  • the present disclosure relates to image processing technology, and particularly to a depth image denoising method and denoising apparatus.
  • a depth image of a shot object is obtained usually by a visual imaging apparatus having a pair of cameras (for example, a binocular recognition system).
  • noise(s) is/are always an important factor that affects accuracy of the computation.
  • Conventional denoising method usually searches ineffective connectivity region of smaller area, for example, connectivity region of an area less than five pixel points, within the depth image. These ineffective connectivity regions are regarded automatically as isolated noises (or are named as ineffective points), and then, these isolated noises are removed directly. Nevertheless, some noises are connected to effective connectivity region of greater area, and, by using the conventional denoising method, these noises that are connected to effective connectivity region of greater area will not be eliminated, which reduces the denoising effect.
  • a depth image denoising method comprising the following steps:
  • a depth image denoising apparatus comprising: an image decomposing device configured for decomposing an original depth image into n layers of depth image (M 1 ⁇ Mn), where n is an integer that is greater than or equal to two; an image denoising device configured for denoising on each of the n layers of depth image (M 1 ⁇ Mn), to eliminate isolated noise(s) in each of the n layers of depth image (M 1 ⁇ Mn); and an image merging device configured for merging the denoised n layers of depth image (M 1 ⁇ Mn), to obtain a final denoised depth image.
  • FIG. 1 shows an original depth image of a shot object
  • FIG. 2 shows a depth image obtained by denoising on the original depth image of FIG. 1 , using a conventional denoising method
  • FIG. 3 shows an example of a human body depth image obtained by denoising on a human body depth image, by using a conventional denoising method
  • FIG. 4 shows a corresponding relation between a depth of an original depth image outputted by a visual imaging apparatus and an actual distance of a shot object to the visual imaging apparatus;
  • FIG. 5 shows a principle diagram of decomposing an original depth image into four layers of depth image, by using a depth image denoising method according to an embodiment of the present disclosure
  • FIG. 6 shows an original depth image of a shot object
  • FIGS. 7 a -7 d show four layers of depth image achieved after decomposing the original depth image of FIG. 6 , by using a depth image denoising method according to an embodiment of the present disclosure
  • FIGS. 8 a -8 d show four layers of depth image achieved after denoising on the four layers of depth image of FIGS. 7 a - 7 d;
  • FIG. 9 shows a final depth image obtained after merging the denoised four layers of depth image of FIGS. 8 a - 8 d;
  • FIG. 10 shows a process of denoising on an original depth image, by using a depth image denoising method according to an embodiment of the present disclosure
  • FIG. 11 shows an example of a human body depth image obtained by denoising on a human body depth image, by using a depth image denoising method according to an embodiment of the present disclosure.
  • FIG. 12 shows a block diagram of a depth image denoising apparatus according to an embodiment of the present disclosure.
  • FIG. 1 shows an original depth image of a shot object.
  • FIG. 2 shows a depth image obtained by denoising on the original depth image of FIG. 1 , using a conventional denoising method.
  • noises 11 , 12 , 13 have smaller areas (less than five pixel points), accordingly, in the conventional denoising method, the three noises 11 , 12 , 13 are regarded as isolated noises, and then are removed directly. However, the other two noises 14 , 15 are connected to an effective connectivity region 20 of greater area, accordingly, in the conventional denoising method, the other two noises 14 , 15 are not removed. As a result from this, the two noises 14 , 15 are still remained in the denoised depth image, for example, as shown in FIG. 2 .
  • FIG. 3 shows an example of a human body depth image obtained by denoising on a human body depth image, by using a conventional denoising method.
  • the denoised human body depth image there are several white points (noises) which are connected to the human body. These white points are connected to the human body, accordingly, they cannot be removed in the conventional denoising method, which lowers quality of the human body depth image.
  • a depth image denoising method comprises the following steps: decomposing an original depth image of a shot object into n layers of depth image, where n is an integer that is greater than or equal to two; denoising on each of the n layers of depth image, to eliminate isolated noise(s) in each of the n layers of depth image; and, merging the denoised n layers of depth image, to obtain a final denoised depth image.
  • FIG. 10 shows a process of denoising on an original depth image, by using a depth image denoising method according to an embodiment of the present disclosure.
  • the process of denoising on an original depth image mainly comprises the followings steps:
  • FIG. 6 shows an original depth image to be denoised.
  • the original depth image shown in FIG. 6 is completely the same as the original depth image shown in FIG. 1 .
  • a visual imaging apparatus for example, a binocular recognition system including a pair of cameras or a monocular recognition system having a single camera, can be used, to obtain an original depth image of a shot object.
  • a binocular recognition system is generally used to obtain an original depth image of a shot object.
  • the binocular recognition system obtains an original depth image of a shot object, by shooting the object simultaneously using double cameras, and calculating a three-dimensional coordinate of this object according to a positional relationship of the object on the images from left and right cameras and a spacing between the cameras.
  • the original depth image comprises a plurality of pixels points arranged in array, for example, 1024*1024pixels points, and a depth of each of the pixels points is indicated as grey level (which is divided into 0-256 levels, 0 denotes pure black and 256 denotes pure white.
  • the process of obtaining an original depth image of a shot object by using a binocular recognition system generally comprises the followings steps: arranging the pair of cameras at either side of the shot object symmetrically; shooting the shot object simultaneously by using the pair of cameras, to obtain two images of the shot object; and, obtaining the original depth image of the shot object in accordance with the two images shot simultaneously by using the pair of cameras.
  • distances of these points of the shot object to the camera can be calculated according to depths of these pixel points in the original depth image of the shot object, since there is certain mapping relationship between the two.
  • FIG. 4 shows a corresponding relation between a depth of an original depth image outputted by a visual imaging apparatus and an actual distance of a shot object to the visual imaging apparatus (camera).
  • horizontal coordinate x represents a value (grey level) of the depth of an original depth image outputted
  • longitudinal coordinate y represents an actual distance (in millimeters) of a shot object to a visual imaging apparatus (camera).
  • FIG. 5 shows a principle diagram of decomposing an original depth image into a plurality of layers of depth image.
  • the value of the depth of the original depth image outputted gradually goes smaller as the actual distance of the shot object to the visual imaging apparatus (camera) gradually goes greater.
  • the actual distance of the shot object to the visual imaging apparatus is required to be within a suitable range.
  • the actual distance of the shot object to the visual imaging apparatus is required to be within a range of 1 m to 4 m, since the depth range that corresponds to the distance range of 1 m to 4 m is the one within which depth information is much more concentrated.
  • the region within which depth information is much more concentrated is named as a preset depth region [X 1 , X 2 ], while the one that corresponds to the preset depth region [X 1 , X 2 ] is an actual distance region [Y 2 , Y 1 ].
  • an original depth image for example, an original depth image shown in FIG. 6
  • 11 , 12 , 13 represent three isolated noises separated from an effective connectivity region 20 of greater area
  • 14 , 15 represent two noises connected to the effective connectivity region 20 of greater area.
  • an actual distance region [Y 2 , Y 1 ] that corresponds to the preset depth region [X 1 , X 2 ] of the original depth image is obtained.
  • the actual distance region [Y 2 , Y 1 ] that corresponds to the preset depth region [X 1 , X 2 ] of the original depth image is divided equally into n distance intervals B 1 ⁇ Bn, where n is an integer that is greater than or equal to two, as shown in
  • n is set to be equal to four. That is, the actual distance region [Y 2 , Y 1 ] is divided equally into four distance intervals B 1 , B 2 , B 3 , B 4 . Please be noted that, interval lengths of the four distance intervals B 1 , B 2 , B 3 , B 4 are equal to one another.
  • the preset depth region [X 1 , X 2 ] of the original depth image is divided into n depth intervals A 1 ⁇ An which correspond respectively to the n distance intervals B 1 ⁇ Bn, as shown in FIG. 5 .
  • the preset depth region [X 1 , X 2 ] is divided into four depth intervals A 1 , A 2 , A 3 , A 4 .
  • interval lengths of the four depth intervals A 1 , A 2 , A 3 , A 4 are not equal. Specifically, interval lengths of the four depth intervals A 1 , A 2 , A 3 , A 4 are increased in turn.
  • interval length of the depth interval A 2 is greater than interval length of the depth interval A 1
  • interval length of the depth interval A 3 is greater than interval length of the depth interval A 2
  • interval length of the depth interval A 4 is greater than interval length of the depth interval A 3 .
  • the original depth image is decomposed into n layers of depth image M 1 ⁇ Mn which correspond respectively to the n depth intervals A 1 ⁇ An.
  • the original depth image is decomposed into four layers of depth image M 1 , M 2 , M 3 , M 4 . That is, a first layer of depth image M 1 corresponds to a first depth interval A 1 , a second layer of depth image M 2 corresponds to a second depth interval A 2 , a third layer of depth image M 3 corresponds to a third depth interval A 3 , and a fourth layer of depth image M 4 corresponds to a fourth depth interval A 4 .
  • the original depth image of FIG. 6 is decomposed into four layers of depth image M 1 , M 2 , M 3 , M 4 , shown in FIGS. 7 a -7 d .
  • values of the depths of noises 13 , 14 in the original depth image are within the first depth interval A 1 , accordingly, the noises 13 , 14 are placed within corresponding pixel point positions of the first layer of depth image M 1 as shown in FIG. 7 a , while values of the depths of the rest pixel point positions of the first layer of depth image M 1 are all set to zero.
  • values of the depths of noises 12 , 15 in the original depth image are within the second depth interval A 2 , accordingly, the noises 12 , 15 are placed within corresponding pixel point positions of the second layer of depth image M 2 as shown in FIG. 7 b , while values of the depths of the rest pixel point positions of the second layer of depth image M 2 are all set to zero.
  • a value of the depth of noise 11 in the original depth image is within the third depth interval A 3 , accordingly, the noise 11 is placed within a corresponding pixel point position of the third layer of depth image M 3 as shown in FIG. 7 c , while values of the depths of the rest pixel point positions of the third layer of depth image M 3 are all set to zero.
  • a value of the depth of an effective connectivity region 20 of greater area in the original depth image are within the fourth depth interval A 4 , accordingly, the effective connectivity region 20 is placed within corresponding pixel point positions of the fourth layer of depth image M 4 as shown in FIG. 7 d , while values of the depths of the rest pixel point positions of the fourth layer of depth image M 4 are all set to zero.
  • the original depth image of FIG. 6 is decomposed into four layers of depth image M 1 , M 2 , M 3 , M 4 , shown in FIGS. 7 a - 7 d.
  • denoising processings are performed on the four layers of depth image M 1 , M 2 , M 3 , M 4 , shown in FIGS. 7 a -7 d , in sequence, to eliminate isolated noise(s) in each of the four layers of depth image M 1 , M 2 , M 3 , M 4 .
  • all the noises 11 , 12 , 13 , 14 , 15 in FIG. 7 a , FIG. 7 b , FIGS. 7 c and 7 d will be eliminated, to obtain denoised four layers of depth image M 1 , M 2 , M 3 , M 4 , as shown in FIGS. 8 a -8 d .
  • FIGS. 8 a -8 d Referring to FIGS.
  • FIG. 11 shows an example of a human body depth image obtained by denoising on a human body depth image, by using a depth image denoising method according to an embodiment of the present disclosure.
  • the noise(s) which is/are connected to the human body is/are eliminated, thereby improving quality of the denoised depth image.
  • the original depth image of FIG. 6 is decomposed into four layers of depth image.
  • the present disclosure is not limited to these embodiments shown, and, the original depth image can be decomposed into two layers, three layers, five layers or more layers.
  • the optimal number of layers is determined in accordance with the denoising effect and the denoising speed.
  • the original depth image is usually decomposed into 12 layers or less than 12 layers.
  • an upper limit value of the number n is related to a processing speed of the host computer, accordingly, for a host computer with greater processing capacity, an upper limit value of the number n may be greater than 12.
  • FIG. 12 shows a block diagram of a depth image denoising apparatus according to an embodiment of the present disclosure.
  • a depth image denoising apparatus which corresponds to the abovementioned depth image denoising method, is also disclosed.
  • the denoising apparatus mainly comprises: an image decomposing device configured for decomposing an original depth image into n layers of depth image M 1 ⁇ Mn, where n is an integer that is greater than or equal to two; an image denoising device configured for denoising on each of the n layers of depth image M 1 ⁇ Mn, to eliminate isolated noise(s) in each of the n layers of depth image M 1 ⁇ Mn; and an image merging device configured for merging the denoised n layers of depth image M 1 ⁇ Mn, to obtain a final denoised depth image.
  • the image decomposing device may comprise: a distance region obtaining module, a distance region equally-dividing module, a depth region dividing module and a depth image decomposing module.
  • the abovementioned distance region obtaining module is for obtaining an actual distance region [Y 2 , Y 1 ] that corresponds to a preset depth region [X 1 , X 2 ] of the original depth image, in accordance with a corresponding relation between a depth x of the original depth image and an actual distance y of the shot object to a visual imaging apparatus.
  • the abovementioned distance region equally-dividing module is for dividing equally the actual distance region [Y 2 , Y 1 ] that corresponds to the preset depth region [X 1 , X 2 ] of the original depth image into n distance intervals B 1 ⁇ Bn.
  • the abovementioned depth region dividing module is for dividing the preset depth region [X 1 , X 2 ] of the original depth image into n depth intervals A 1 ⁇ An which correspond respectively to the n distance intervals B 1 ⁇ Bn.
  • the abovementioned depth image decomposing module is for decomposing the original depth image into the n layers of depth image M 1 ⁇ Mn which correspond respectively to the n depth intervals A 1 ⁇ An. Further, the abovementioned depth image decomposing module may be configured for: extracting a pixel point that corresponds to a depth interval Ai of an i th layer of depth image Mi, from the original depth image, and, placing the extracted pixel point into a corresponding pixel point position in the i th layer of depth image Mi, the rest pixel point positions in the i th layer of depth image Mi being set to zero, where 1 ⁇ i ⁇ n. Furthermore, a value of the number n is determined in accordance with a denoising effect and a denoising speed.
  • the actual distance y of the shot object to the visual imaging apparatus is within a range of 0 ⁇ 10 m
  • a value of the depth of the original depth image is within a range of 0 ⁇ 256
  • the actual distance region [Y 2 , Y 1 ] that corresponds to the preset depth region [X 1 , X 2 ] of the original depth image is chosen to be [1 m, 4 m].
  • the visual imaging apparatus by which the original depth image of the shot object is obtained may comprise a pair of cameras. Further, the pair of cameras are arranged at either side of the shot object symmetrically, the shot object is shot simultaneously by the pair of cameras, and, the original depth image of the shot object is obtained in accordance with two images shot simultaneously by the pair of cameras.

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